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As one of the most prevalent cancers among women worldwide, breast cancer has attracted the most attention by researchers. It has been verified that an accurate and early detection of breast cancer can increase the chances for the patients to take the right treatment plan and survive for a long time. Nowadays, numerous classification methods have been utilized for breast cancer diagnosis. However, most of these classification models have concentrated on maximum the classification accuracy, failed to take into account the unequal misclassification costs for the breast cancer diagnosis. To the best of our knowledge, misclassifying the cancerous patient as non-cancerous has much higher cost compared to misclassifying the non-cancerous as cancerous. Consequently, in order to tackle this deficiency and further improve the classification accuracy of the breast cancer diagnosis, we propose an improved cost-sensitive support vector machine classifier (ICS-SVM) for the diagnosis of breast cancer. In the proposed approach, we take full account of unequal misclassification costs of breast cancer intelligent diagnosis and provide more reasonable results over previous works and conventional classification models. To evaluate the performance of the proposed approach, Wisconsin Breast Cancer (WBC) and Wisconsin Diagnostic Breast Cancer (WDBC) breast cancer datasets obtained from the University of California at Irvine (UCI) machine learning repository have been studied. The experimental results demonstrate that the proposed hybrid algorithm outperforms all the existing methods. Promisingly, the proposed method can be regarded as a useful clinical tool for breast cancer diagnosis and could also be applied to other illness diagnosis.

Background Online patient communities provide new channels for users to access and share medical information. In-depth study of users’ willingness to share information in online patient communities is of great significance for improving the level of information sharing among the patient community and the long-term development of communities. Objective The aim of this study was to build a model of factors affecting patients’ willingness to share medical information from the perspective of both positive and negative utilities. Specifically, we aimed to determine the influence of online information support and privacy concerns, as well as the moderating effect of disease severity and information sensitivity of different patients on their willingness to share. Methods Data from 490 users with experience in online patient communities were collected through a questionnaire survey, and structural equations were applied to empirically verify the model hypotheses. Results Privacy concerns negatively affected the patients’ willingness to share information (P<.001), whereas online information support positively affected patients’ willingness to share information (P<.001), and information sensitivity negatively moderated the impact of online information support on sharing willingness (P=.01). Disease severity positively moderated the impact of privacy concerns on sharing willingness (P=.05). However, the hypotheses that information sensitivity is a negative moderator and disease severity is a positive moderator of the impact of privacy concerns on sharing willingness could not be supported. Conclusions To improve the level of user information sharing, the online patient community should design a safe user registration process, ensure the confidentiality of information, reduce the privacy concerns of users, and accurately identify the information needs of patients to provide personalized support services.

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